Overview

Dataset statistics

Number of variables21
Number of observations9551
Missing cells9
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.5 MiB
Average record size in memory168.0 B

Variable types

Numeric7
Text6
Categorical4
Boolean4

Alerts

Switch to order menu has constant value ""Constant
Country Code is highly overall correlated with CurrencyHigh correlation
Longitude is highly overall correlated with CurrencyHigh correlation
Latitude is highly overall correlated with CurrencyHigh correlation
Aggregate rating is highly overall correlated with Votes and 2 other fieldsHigh correlation
Votes is highly overall correlated with Aggregate ratingHigh correlation
Currency is highly overall correlated with Country Code and 2 other fieldsHigh correlation
Has Table booking is highly overall correlated with Price rangeHigh correlation
Price range is highly overall correlated with Has Table bookingHigh correlation
Rating color is highly overall correlated with Aggregate rating and 1 other fieldsHigh correlation
Rating text is highly overall correlated with Aggregate rating and 1 other fieldsHigh correlation
Currency is highly imbalanced (81.0%)Imbalance
Is delivering now is highly imbalanced (96.6%)Imbalance
Average Cost for two is highly skewed (γ1 = 35.4779149)Skewed
Restaurant ID has unique valuesUnique
Longitude has 498 (5.2%) zerosZeros
Latitude has 498 (5.2%) zerosZeros
Aggregate rating has 2148 (22.5%) zerosZeros
Votes has 1094 (11.5%) zerosZeros

Reproduction

Analysis started2023-08-03 06:47:31.013091
Analysis finished2023-08-03 06:47:42.173243
Duration11.16 seconds
Software versionydata-profiling vv4.4.0
Download configurationconfig.json

Variables

Restaurant ID
Real number (ℝ)

UNIQUE 

Distinct9551
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9051128.3
Minimum53
Maximum18500652
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size74.7 KiB
2023-08-03T11:47:42.355144image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum53
5-th percentile2199.5
Q1301962.5
median6004089
Q318352292
95-th percentile18458642
Maximum18500652
Range18500599
Interquartile range (IQR)18050329

Descriptive statistics

Standard deviation8791521.3
Coefficient of variation (CV)0.97131771
Kurtosis-1.9509964
Mean9051128.3
Median Absolute Deviation (MAD)6003111
Skewness0.061569976
Sum8.6447327 × 1010
Variance7.7290846 × 1013
MonotonicityNot monotonic
2023-08-03T11:47:42.506709image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6317637 1
 
< 0.1%
18254520 1
 
< 0.1%
18462589 1
 
< 0.1%
18336474 1
 
< 0.1%
18336477 1
 
< 0.1%
18382047 1
 
< 0.1%
18441566 1
 
< 0.1%
18441669 1
 
< 0.1%
18396358 1
 
< 0.1%
6248 1
 
< 0.1%
Other values (9541) 9541
99.9%
ValueCountFrequency (%)
53 1
< 0.1%
55 1
< 0.1%
60 1
< 0.1%
64 1
< 0.1%
65 1
< 0.1%
66 1
< 0.1%
67 1
< 0.1%
69 1
< 0.1%
73 1
< 0.1%
89 1
< 0.1%
ValueCountFrequency (%)
18500652 1
< 0.1%
18500639 1
< 0.1%
18500628 1
< 0.1%
18500618 1
< 0.1%
18499493 1
< 0.1%
18499482 1
< 0.1%
18499475 1
< 0.1%
18499474 1
< 0.1%
18499472 1
< 0.1%
18499471 1
< 0.1%

Restaurant Name
Text

Distinct7446
Distinct (%)78.0%
Missing0
Missing (%)0.0%
Memory size74.7 KiB
2023-08-03T11:47:43.100100image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Length

Max length54
Median length46
Mean length15.164171
Min length2

Characters and Unicode

Total characters144833
Distinct characters85
Distinct categories12 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6712 ?
Unique (%)70.3%

Sample

1st rowLe Petit Souffle
2nd rowIzakaya Kikufuji
3rd rowHeat - Edsa Shangri-La
4th rowOoma
5th rowSambo Kojin
ValueCountFrequency (%)
931
 
3.8%
the 780
 
3.2%
cafe 615
 
2.5%
restaurant 455
 
1.9%
food 391
 
1.6%
corner 294
 
1.2%
pizza 240
 
1.0%
sweets 232
 
0.9%
kitchen 224
 
0.9%
bar 208
 
0.8%
Other values (5988) 20178
82.2%
2023-08-03T11:47:43.858905image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 15445
 
10.7%
15010
 
10.4%
e 11821
 
8.2%
i 8195
 
5.7%
r 7362
 
5.1%
n 7307
 
5.0%
o 7285
 
5.0%
t 5928
 
4.1%
s 5882
 
4.1%
h 5418
 
3.7%
Other values (75) 55180
38.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 102725
70.9%
Uppercase Letter 23959
 
16.5%
Space Separator 15010
 
10.4%
Other Punctuation 1785
 
1.2%
Dash Punctuation 623
 
0.4%
Decimal Number 460
 
0.3%
Other Symbol 212
 
0.1%
Connector Punctuation 38
 
< 0.1%
Math Symbol 6
 
< 0.1%
Open Punctuation 6
 
< 0.1%
Other values (2) 9
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 15445
15.0%
e 11821
11.5%
i 8195
 
8.0%
r 7362
 
7.2%
n 7307
 
7.1%
o 7285
 
7.1%
t 5928
 
5.8%
s 5882
 
5.7%
h 5418
 
5.3%
l 4360
 
4.2%
Other values (16) 23722
23.1%
Uppercase Letter
ValueCountFrequency (%)
C 3395
14.2%
B 2253
 
9.4%
S 2193
 
9.2%
T 1685
 
7.0%
P 1450
 
6.1%
D 1442
 
6.0%
R 1294
 
5.4%
M 1274
 
5.3%
F 1208
 
5.0%
K 1157
 
4.8%
Other values (16) 6608
27.6%
Other Punctuation
ValueCountFrequency (%)
' 1033
57.9%
& 487
27.3%
. 159
 
8.9%
! 52
 
2.9%
, 20
 
1.1%
@ 11
 
0.6%
# 7
 
0.4%
: 6
 
0.3%
/ 6
 
0.3%
" 2
 
0.1%
Other values (2) 2
 
0.1%
Decimal Number
ValueCountFrequency (%)
2 81
17.6%
1 79
17.2%
4 59
12.8%
3 49
10.7%
6 45
9.8%
7 36
7.8%
9 33
7.2%
5 29
 
6.3%
0 21
 
4.6%
8 17
 
3.7%
Open Punctuation
ValueCountFrequency (%)
( 5
83.3%
{ 1
 
16.7%
Close Punctuation
ValueCountFrequency (%)
) 5
83.3%
} 1
 
16.7%
Space Separator
ValueCountFrequency (%)
15010
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 623
100.0%
Other Symbol
ValueCountFrequency (%)
� 212
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 38
100.0%
Math Symbol
ValueCountFrequency (%)
+ 6
100.0%
Currency Symbol
ValueCountFrequency (%)
$ 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 126684
87.5%
Common 18138
 
12.5%
Arabic 11
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 15445
 
12.2%
e 11821
 
9.3%
i 8195
 
6.5%
r 7362
 
5.8%
n 7307
 
5.8%
o 7285
 
5.8%
t 5928
 
4.7%
s 5882
 
4.6%
h 5418
 
4.3%
l 4360
 
3.4%
Other values (42) 47681
37.6%
Common
ValueCountFrequency (%)
15010
82.8%
' 1033
 
5.7%
- 623
 
3.4%
& 487
 
2.7%
� 212
 
1.2%
. 159
 
0.9%
2 81
 
0.4%
1 79
 
0.4%
4 59
 
0.3%
! 52
 
0.3%
Other values (22) 343
 
1.9%
Arabic
ValueCountFrequency (%)
Û± 11
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 144609
99.8%
Specials 212
 
0.1%
Arabic 11
 
< 0.1%
None 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 15445
 
10.7%
15010
 
10.4%
e 11821
 
8.2%
i 8195
 
5.7%
r 7362
 
5.1%
n 7307
 
5.1%
o 7285
 
5.0%
t 5928
 
4.1%
s 5882
 
4.1%
h 5418
 
3.7%
Other values (72) 54956
38.0%
Specials
ValueCountFrequency (%)
� 212
100.0%
Arabic
ValueCountFrequency (%)
Û± 11
100.0%
None
ValueCountFrequency (%)
· 1
100.0%

Country Code
Real number (ℝ)

HIGH CORRELATION 

Distinct15
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.365616
Minimum1
Maximum216
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size74.7 KiB
2023-08-03T11:47:44.000033image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile215
Maximum216
Range215
Interquartile range (IQR)0

Descriptive statistics

Standard deviation56.750546
Coefficient of variation (CV)3.0900431
Kurtosis7.3925784
Mean18.365616
Median Absolute Deviation (MAD)0
Skewness3.0439653
Sum175410
Variance3220.6244
MonotonicityNot monotonic
2023-08-03T11:47:44.096565image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
1 8652
90.6%
216 434
 
4.5%
215 80
 
0.8%
30 60
 
0.6%
214 60
 
0.6%
189 60
 
0.6%
148 40
 
0.4%
208 34
 
0.4%
14 24
 
0.3%
162 22
 
0.2%
Other values (5) 85
 
0.9%
ValueCountFrequency (%)
1 8652
90.6%
14 24
 
0.3%
30 60
 
0.6%
37 4
 
< 0.1%
94 21
 
0.2%
148 40
 
0.4%
162 22
 
0.2%
166 20
 
0.2%
184 20
 
0.2%
189 60
 
0.6%
ValueCountFrequency (%)
216 434
4.5%
215 80
 
0.8%
214 60
 
0.6%
208 34
 
0.4%
191 20
 
0.2%
189 60
 
0.6%
184 20
 
0.2%
166 20
 
0.2%
162 22
 
0.2%
148 40
 
0.4%

City
Text

Distinct141
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size74.7 KiB
2023-08-03T11:47:44.764239image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Length

Max length22
Median length9
Mean length8.1586221
Min length3

Characters and Unicode

Total characters77923
Distinct characters54
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique46 ?
Unique (%)0.5%

Sample

1st rowMakati City
2nd rowMakati City
3rd rowMandaluyong City
4th rowMandaluyong City
5th rowMandaluyong City
ValueCountFrequency (%)
new 5473
35.7%
delhi 5473
35.7%
gurgaon 1118
 
7.3%
noida 1080
 
7.0%
faridabad 251
 
1.6%
city 82
 
0.5%
ghaziabad 25
 
0.2%
lucknow 21
 
0.1%
bay 21
 
0.1%
guwahati 21
 
0.1%
Other values (155) 1759
 
11.5%
2023-08-03T11:47:45.375189image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 11659
15.0%
i 7587
9.7%
N 6593
8.5%
h 5997
 
7.7%
l 5979
 
7.7%
5773
 
7.4%
D 5634
 
7.2%
w 5598
 
7.2%
a 4948
 
6.3%
o 3096
 
4.0%
Other values (44) 15059
19.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 56730
72.8%
Uppercase Letter 15291
 
19.6%
Space Separator 5773
 
7.4%
Other Symbol 88
 
0.1%
Other Punctuation 20
 
< 0.1%
Connector Punctuation 20
 
< 0.1%
Dash Punctuation 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 11659
20.6%
i 7587
13.4%
h 5997
10.6%
l 5979
10.5%
w 5598
9.9%
a 4948
8.7%
o 3096
 
5.5%
r 2126
 
3.7%
d 2052
 
3.6%
n 2016
 
3.6%
Other values (16) 5672
10.0%
Uppercase Letter
ValueCountFrequency (%)
N 6593
43.1%
D 5634
36.8%
G 1204
 
7.9%
F 254
 
1.7%
C 226
 
1.5%
A 223
 
1.5%
P 153
 
1.0%
B 150
 
1.0%
S 140
 
0.9%
M 135
 
0.9%
Other values (13) 579
 
3.8%
Space Separator
ValueCountFrequency (%)
5773
100.0%
Other Symbol
ValueCountFrequency (%)
� 88
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 20
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 20
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 72021
92.4%
Common 5902
 
7.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 11659
16.2%
i 7587
10.5%
N 6593
9.2%
h 5997
8.3%
l 5979
8.3%
D 5634
7.8%
w 5598
7.8%
a 4948
 
6.9%
o 3096
 
4.3%
r 2126
 
3.0%
Other values (39) 12804
17.8%
Common
ValueCountFrequency (%)
5773
97.8%
� 88
 
1.5%
/ 20
 
0.3%
_ 20
 
0.3%
- 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 77835
99.9%
Specials 88
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 11659
15.0%
i 7587
9.7%
N 6593
8.5%
h 5997
7.7%
l 5979
 
7.7%
5773
 
7.4%
D 5634
 
7.2%
w 5598
 
7.2%
a 4948
 
6.4%
o 3096
 
4.0%
Other values (43) 14971
19.2%
Specials
ValueCountFrequency (%)
� 88
100.0%

Address
Text

Distinct8918
Distinct (%)93.4%
Missing0
Missing (%)0.0%
Memory size74.7 KiB
2023-08-03T11:47:46.094206image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Length

Max length132
Median length102
Mean length53.536698
Min length13

Characters and Unicode

Total characters511329
Distinct characters76
Distinct categories11 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8541 ?
Unique (%)89.4%

Sample

1st rowThird Floor, Century City Mall, Kalayaan Avenue, Poblacion, Makati City
2nd rowLittle Tokyo, 2277 Chino Roces Avenue, Legaspi Village, Makati City
3rd rowEdsa Shangri-La, 1 Garden Way, Ortigas, Mandaluyong City
4th rowThird Floor, Mega Fashion Hall, SM Megamall, Ortigas, Mandaluyong City
5th rowThird Floor, Mega Atrium, SM Megamall, Ortigas, Mandaluyong City
ValueCountFrequency (%)
new 5737
 
6.9%
delhi 5628
 
6.7%
sector 2092
 
2.5%
road 1956
 
2.3%
nagar 1804
 
2.2%
market 1734
 
2.1%
near 1681
 
2.0%
floor 1340
 
1.6%
gurgaon 1165
 
1.4%
noida 1146
 
1.4%
Other values (9053) 59200
70.9%
2023-08-03T11:47:46.991750image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
74018
 
14.5%
a 45036
 
8.8%
e 33224
 
6.5%
, 31658
 
6.2%
r 26155
 
5.1%
o 23614
 
4.6%
i 22676
 
4.4%
l 19476
 
3.8%
h 16369
 
3.2%
n 16158
 
3.2%
Other values (66) 202945
39.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 293424
57.4%
Uppercase Letter 79226
 
15.5%
Space Separator 74018
 
14.5%
Other Punctuation 33556
 
6.6%
Decimal Number 27534
 
5.4%
Dash Punctuation 2884
 
0.6%
Other Symbol 311
 
0.1%
Open Punctuation 154
 
< 0.1%
Close Punctuation 154
 
< 0.1%
Connector Punctuation 62
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 45036
15.3%
e 33224
11.3%
r 26155
8.9%
o 23614
 
8.0%
i 22676
 
7.7%
l 19476
 
6.6%
h 16369
 
5.6%
n 16158
 
5.5%
t 15998
 
5.5%
d 10270
 
3.5%
Other values (16) 64448
22.0%
Uppercase Letter
ValueCountFrequency (%)
N 11717
14.8%
D 8344
10.5%
S 7927
10.0%
M 6408
 
8.1%
C 5374
 
6.8%
G 4940
 
6.2%
P 4243
 
5.4%
A 3971
 
5.0%
B 3760
 
4.7%
R 3416
 
4.3%
Other values (16) 19126
24.1%
Decimal Number
ValueCountFrequency (%)
1 6551
23.8%
2 4191
15.2%
3 3419
12.4%
4 2355
 
8.6%
5 2324
 
8.4%
0 2157
 
7.8%
6 1855
 
6.7%
7 1686
 
6.1%
8 1629
 
5.9%
9 1341
 
4.9%
Other Punctuation
ValueCountFrequency (%)
, 31658
94.3%
/ 1274
 
3.8%
. 301
 
0.9%
& 278
 
0.8%
' 39
 
0.1%
# 6
 
< 0.1%
Space Separator
ValueCountFrequency (%)
74018
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2884
100.0%
Other Symbol
ValueCountFrequency (%)
� 311
100.0%
Open Punctuation
ValueCountFrequency (%)
( 154
100.0%
Close Punctuation
ValueCountFrequency (%)
) 154
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 62
100.0%
Control
ValueCountFrequency (%)
6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 372650
72.9%
Common 138653
 
27.1%
Arabic 26
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 45036
 
12.1%
e 33224
 
8.9%
r 26155
 
7.0%
o 23614
 
6.3%
i 22676
 
6.1%
l 19476
 
5.2%
h 16369
 
4.4%
n 16158
 
4.3%
t 15998
 
4.3%
N 11717
 
3.1%
Other values (42) 142227
38.2%
Common
ValueCountFrequency (%)
74018
53.4%
, 31658
22.8%
1 6551
 
4.7%
2 4191
 
3.0%
3 3419
 
2.5%
- 2884
 
2.1%
4 2355
 
1.7%
5 2324
 
1.7%
0 2157
 
1.6%
6 1855
 
1.3%
Other values (13) 7241
 
5.2%
Arabic
ValueCountFrequency (%)
Û± 26
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 510992
99.9%
Specials 311
 
0.1%
Arabic 26
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
74018
 
14.5%
a 45036
 
8.8%
e 33224
 
6.5%
, 31658
 
6.2%
r 26155
 
5.1%
o 23614
 
4.6%
i 22676
 
4.4%
l 19476
 
3.8%
h 16369
 
3.2%
n 16158
 
3.2%
Other values (64) 202608
39.6%
Specials
ValueCountFrequency (%)
� 311
100.0%
Arabic
ValueCountFrequency (%)
Û± 26
100.0%

Locality
Text

Distinct1208
Distinct (%)12.6%
Missing0
Missing (%)0.0%
Memory size74.7 KiB
2023-08-03T11:47:47.647167image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Length

Max length51
Median length45
Mean length14.013297
Min length3

Characters and Unicode

Total characters133841
Distinct characters75
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique551 ?
Unique (%)5.8%

Sample

1st rowCentury City Mall, Poblacion, Makati City
2nd rowLittle Tokyo, Legaspi Village, Makati City
3rd rowEdsa Shangri-La, Ortigas, Mandaluyong City
4th rowSM Megamall, Ortigas, Mandaluyong City
5th rowSM Megamall, Ortigas, Mandaluyong City
ValueCountFrequency (%)
sector 1627
 
7.2%
nagar 1190
 
5.3%
vihar 613
 
2.7%
mall 588
 
2.6%
road 488
 
2.2%
phase 463
 
2.0%
dlf 382
 
1.7%
1 368
 
1.6%
place 314
 
1.4%
colony 266
 
1.2%
Other values (1390) 16292
72.1%
2023-08-03T11:47:48.438818image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 17914
 
13.4%
13084
 
9.8%
r 9236
 
6.9%
e 7915
 
5.9%
i 6287
 
4.7%
o 6275
 
4.7%
t 5667
 
4.2%
n 5554
 
4.1%
l 5109
 
3.8%
h 4677
 
3.5%
Other values (65) 52123
38.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 92677
69.2%
Uppercase Letter 21758
 
16.3%
Space Separator 13084
 
9.8%
Decimal Number 4188
 
3.1%
Other Punctuation 1684
 
1.3%
Close Punctuation 140
 
0.1%
Open Punctuation 140
 
0.1%
Dash Punctuation 78
 
0.1%
Other Symbol 68
 
0.1%
Connector Punctuation 24
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 17914
19.3%
r 9236
10.0%
e 7915
 
8.5%
i 6287
 
6.8%
o 6275
 
6.8%
t 5667
 
6.1%
n 5554
 
6.0%
l 5109
 
5.5%
h 4677
 
5.0%
u 3025
 
3.3%
Other values (16) 21018
22.7%
Uppercase Letter
ValueCountFrequency (%)
S 3165
14.5%
P 1946
 
8.9%
N 1845
 
8.5%
M 1789
 
8.2%
C 1667
 
7.7%
G 1359
 
6.2%
K 1213
 
5.6%
V 1141
 
5.2%
R 992
 
4.6%
L 922
 
4.2%
Other values (16) 5719
26.3%
Decimal Number
ValueCountFrequency (%)
1 1136
27.1%
2 772
18.4%
3 566
13.5%
5 386
 
9.2%
4 334
 
8.0%
6 250
 
6.0%
8 236
 
5.6%
7 218
 
5.2%
9 141
 
3.4%
0 140
 
3.3%
Other Punctuation
ValueCountFrequency (%)
, 1638
97.3%
& 18
 
1.1%
' 10
 
0.6%
. 10
 
0.6%
/ 6
 
0.4%
: 2
 
0.1%
Space Separator
ValueCountFrequency (%)
13084
100.0%
Close Punctuation
ValueCountFrequency (%)
) 140
100.0%
Open Punctuation
ValueCountFrequency (%)
( 140
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 78
100.0%
Other Symbol
ValueCountFrequency (%)
� 68
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 24
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 114435
85.5%
Common 19397
 
14.5%
Arabic 9
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 17914
15.7%
r 9236
 
8.1%
e 7915
 
6.9%
i 6287
 
5.5%
o 6275
 
5.5%
t 5667
 
5.0%
n 5554
 
4.9%
l 5109
 
4.5%
h 4677
 
4.1%
S 3165
 
2.8%
Other values (42) 42636
37.3%
Common
ValueCountFrequency (%)
13084
67.5%
, 1638
 
8.4%
1 1136
 
5.9%
2 772
 
4.0%
3 566
 
2.9%
5 386
 
2.0%
4 334
 
1.7%
6 250
 
1.3%
8 236
 
1.2%
7 218
 
1.1%
Other values (12) 777
 
4.0%
Arabic
ValueCountFrequency (%)
Û± 9
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 133764
99.9%
Specials 68
 
0.1%
Arabic 9
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 17914
 
13.4%
13084
 
9.8%
r 9236
 
6.9%
e 7915
 
5.9%
i 6287
 
4.7%
o 6275
 
4.7%
t 5667
 
4.2%
n 5554
 
4.2%
l 5109
 
3.8%
h 4677
 
3.5%
Other values (63) 52046
38.9%
Specials
ValueCountFrequency (%)
� 68
100.0%
Arabic
ValueCountFrequency (%)
Û± 9
100.0%

Locality Verbose
Text

Distinct1265
Distinct (%)13.2%
Missing0
Missing (%)0.0%
Memory size74.7 KiB
2023-08-03T11:47:49.115193image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Length

Max length64
Median length56
Mean length24.168569
Min length7

Characters and Unicode

Total characters230834
Distinct characters75
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique566 ?
Unique (%)5.9%

Sample

1st rowCentury City Mall, Poblacion, Makati City, Makati City
2nd rowLittle Tokyo, Legaspi Village, Makati City, Makati City
3rd rowEdsa Shangri-La, Ortigas, Mandaluyong City, Mandaluyong City
4th rowSM Megamall, Ortigas, Mandaluyong City, Mandaluyong City
5th rowSM Megamall, Ortigas, Mandaluyong City, Mandaluyong City
ValueCountFrequency (%)
new 5580
 
14.7%
delhi 5578
 
14.7%
sector 1627
 
4.3%
noida 1246
 
3.3%
gurgaon 1202
 
3.2%
nagar 1190
 
3.1%
vihar 613
 
1.6%
mall 588
 
1.6%
road 488
 
1.3%
phase 463
 
1.2%
Other values (1449) 19343
51.0%
2023-08-03T11:47:49.947959image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
28404
 
12.3%
a 22862
 
9.9%
e 19574
 
8.5%
i 13874
 
6.0%
r 11362
 
4.9%
, 11185
 
4.8%
l 11088
 
4.8%
h 10674
 
4.6%
o 9363
 
4.1%
N 8438
 
3.7%
Other values (65) 84010
36.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 149387
64.7%
Uppercase Letter 37045
 
16.0%
Space Separator 28404
 
12.3%
Other Punctuation 11251
 
4.9%
Decimal Number 4188
 
1.8%
Other Symbol 156
 
0.1%
Open Punctuation 140
 
0.1%
Close Punctuation 140
 
0.1%
Dash Punctuation 79
 
< 0.1%
Connector Punctuation 44
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 22862
15.3%
e 19574
13.1%
i 13874
9.3%
r 11362
 
7.6%
l 11088
 
7.4%
h 10674
 
7.1%
o 9363
 
6.3%
n 7562
 
5.1%
w 6205
 
4.2%
t 6159
 
4.1%
Other values (16) 30664
20.5%
Uppercase Letter
ValueCountFrequency (%)
N 8438
22.8%
D 6556
17.7%
S 3305
 
8.9%
G 2563
 
6.9%
P 2099
 
5.7%
M 1924
 
5.2%
C 1893
 
5.1%
K 1274
 
3.4%
V 1224
 
3.3%
R 1075
 
2.9%
Other values (16) 6694
18.1%
Decimal Number
ValueCountFrequency (%)
1 1136
27.1%
2 772
18.4%
3 566
13.5%
5 386
 
9.2%
4 334
 
8.0%
6 250
 
6.0%
8 236
 
5.6%
7 218
 
5.2%
9 141
 
3.4%
0 140
 
3.3%
Other Punctuation
ValueCountFrequency (%)
, 11185
99.4%
/ 26
 
0.2%
& 18
 
0.2%
' 10
 
0.1%
. 10
 
0.1%
: 2
 
< 0.1%
Space Separator
ValueCountFrequency (%)
28404
100.0%
Other Symbol
ValueCountFrequency (%)
� 156
100.0%
Open Punctuation
ValueCountFrequency (%)
( 140
100.0%
Close Punctuation
ValueCountFrequency (%)
) 140
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 79
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 44
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 186432
80.8%
Common 44393
 
19.2%
Arabic 9
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 22862
 
12.3%
e 19574
 
10.5%
i 13874
 
7.4%
r 11362
 
6.1%
l 11088
 
5.9%
h 10674
 
5.7%
o 9363
 
5.0%
N 8438
 
4.5%
n 7562
 
4.1%
D 6556
 
3.5%
Other values (42) 65079
34.9%
Common
ValueCountFrequency (%)
28404
64.0%
, 11185
 
25.2%
1 1136
 
2.6%
2 772
 
1.7%
3 566
 
1.3%
5 386
 
0.9%
4 334
 
0.8%
6 250
 
0.6%
8 236
 
0.5%
7 218
 
0.5%
Other values (12) 906
 
2.0%
Arabic
ValueCountFrequency (%)
Û± 9
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 230669
99.9%
Specials 156
 
0.1%
Arabic 9
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
28404
 
12.3%
a 22862
 
9.9%
e 19574
 
8.5%
i 13874
 
6.0%
r 11362
 
4.9%
, 11185
 
4.8%
l 11088
 
4.8%
h 10674
 
4.6%
o 9363
 
4.1%
N 8438
 
3.7%
Other values (63) 83845
36.3%
Specials
ValueCountFrequency (%)
� 156
100.0%
Arabic
ValueCountFrequency (%)
Û± 9
100.0%

Longitude
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8120
Distinct (%)85.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64.126574
Minimum-157.94849
Maximum174.83209
Zeros498
Zeros (%)5.2%
Negative578
Negative (%)6.1%
Memory size74.7 KiB
2023-08-03T11:47:50.107707image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-157.94849
5-th percentile-44.979336
Q177.081343
median77.191964
Q377.282006
95-th percentile77.510935
Maximum174.83209
Range332.78058
Interquartile range (IQR)0.20066325

Descriptive statistics

Standard deviation41.467058
Coefficient of variation (CV)0.64664389
Kurtosis8.2165863
Mean64.126574
Median Absolute Deviation (MAD)0.10160718
Skewness-2.8073278
Sum612472.91
Variance1719.5169
MonotonicityNot monotonic
2023-08-03T11:47:50.239544image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 498
 
5.2%
77.3536634 19
 
0.2%
77.2304115 12
 
0.1%
77.0886879 10
 
0.1%
77.2514264 9
 
0.1%
77.3535737 9
 
0.1%
77.3648332 8
 
0.1%
77.2205314 7
 
0.1%
77.218187 7
 
0.1%
77.2041824 6
 
0.1%
Other values (8110) 8966
93.9%
ValueCountFrequency (%)
-157.948486 1
< 0.1%
-157.836031 1
< 0.1%
-157.831538 1
< 0.1%
-157.8312476 1
< 0.1%
-157.831176 1
< 0.1%
-157.827196 1
< 0.1%
-157.825979 1
< 0.1%
-157.822716 1
< 0.1%
-157.813432 1
< 0.1%
-156.693821 1
< 0.1%
ValueCountFrequency (%)
174.8320893 1
< 0.1%
174.810305 1
< 0.1%
174.793257 1
< 0.1%
174.785051 1
< 0.1%
174.782427 1
< 0.1%
174.7806667 1
< 0.1%
174.780345 1
< 0.1%
174.779441 1
< 0.1%
174.7792237 1
< 0.1%
174.7791667 2
< 0.1%

Latitude
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8677
Distinct (%)90.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.854381
Minimum-41.330428
Maximum55.97698
Zeros498
Zeros (%)5.2%
Negative203
Negative (%)2.1%
Memory size74.7 KiB
2023-08-03T11:47:50.383257image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-41.330428
5-th percentile0
Q128.478713
median28.570469
Q328.642758
95-th percentile30.895511
Maximum55.97698
Range97.307408
Interquartile range (IQR)0.1640456

Descriptive statistics

Standard deviation11.007935
Coefficient of variation (CV)0.42576673
Kurtosis12.530803
Mean25.854381
Median Absolute Deviation (MAD)0.07656008
Skewness-3.0816354
Sum246935.19
Variance121.17464
MonotonicityNot monotonic
2023-08-03T11:47:50.525490image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 498
 
5.2%
28.5743086 16
 
0.2%
28.551456 9
 
0.1%
28.5743001 9
 
0.1%
28.5971027 8
 
0.1%
28.625445 7
 
0.1%
28.5503472 6
 
0.1%
28.56904 6
 
0.1%
28.55650347 5
 
0.1%
28.4952079 5
 
0.1%
Other values (8667) 8982
94.0%
ValueCountFrequency (%)
-41.330428 1
< 0.1%
-41.296155 1
< 0.1%
-41.296107 1
< 0.1%
-41.29597 1
< 0.1%
-41.29483333 1
< 0.1%
-41.294565 1
< 0.1%
-41.294402 1
< 0.1%
-41.294234 1
< 0.1%
-41.294154 1
< 0.1%
-41.29383333 1
< 0.1%
ValueCountFrequency (%)
55.97698 1
< 0.1%
55.976644 1
< 0.1%
55.97509722 1
< 0.1%
55.96466944 1
< 0.1%
55.957033 1
< 0.1%
55.95404 1
< 0.1%
55.95349444 1
< 0.1%
55.952221 1
< 0.1%
55.951974 1
< 0.1%
55.949637 1
< 0.1%

Cuisines
Text

Distinct1825
Distinct (%)19.1%
Missing9
Missing (%)0.1%
Memory size74.7 KiB
2023-08-03T11:47:51.218868image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Length

Max length93
Median length77
Mean length19.924963
Min length3

Characters and Unicode

Total characters190124
Distinct characters52
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1278 ?
Unique (%)13.4%

Sample

1st rowFrench, Japanese, Desserts
2nd rowJapanese
3rd rowSeafood, Asian, Filipino, Indian
4th rowJapanese, Sushi
5th rowJapanese, Korean
ValueCountFrequency (%)
indian 4682
16.9%
north 3969
14.4%
food 2855
 
10.3%
chinese 2735
 
9.9%
fast 1987
 
7.2%
mughlai 995
 
3.6%
italian 764
 
2.8%
bakery 745
 
2.7%
continental 736
 
2.7%
cafe 707
 
2.6%
Other values (139) 7480
27.0%
2023-08-03T11:47:51.924403image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
18113
 
9.5%
n 17601
 
9.3%
a 16702
 
8.8%
e 14520
 
7.6%
i 13615
 
7.2%
t 11839
 
6.2%
o 11795
 
6.2%
, 10168
 
5.3%
h 9473
 
5.0%
r 8647
 
4.5%
Other values (42) 57651
30.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 134100
70.5%
Uppercase Letter 27720
 
14.6%
Space Separator 18113
 
9.5%
Other Punctuation 10168
 
5.3%
Dash Punctuation 19
 
< 0.1%
Other Symbol 2
 
< 0.1%
Connector Punctuation 2
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 17601
13.1%
a 16702
12.5%
e 14520
10.8%
i 13615
10.2%
t 11839
8.8%
o 11795
8.8%
h 9473
7.1%
r 8647
6.4%
d 8166
6.1%
s 7950
5.9%
Other values (15) 13792
10.3%
Uppercase Letter
ValueCountFrequency (%)
I 5713
20.6%
F 5002
18.0%
C 4481
16.2%
N 3988
14.4%
M 1894
 
6.8%
S 1729
 
6.2%
B 1641
 
5.9%
A 742
 
2.7%
D 669
 
2.4%
P 426
 
1.5%
Other values (12) 1435
 
5.2%
Space Separator
ValueCountFrequency (%)
18113
100.0%
Other Punctuation
ValueCountFrequency (%)
, 10168
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 19
100.0%
Other Symbol
ValueCountFrequency (%)
� 2
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 161820
85.1%
Common 28304
 
14.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 17601
10.9%
a 16702
10.3%
e 14520
 
9.0%
i 13615
 
8.4%
t 11839
 
7.3%
o 11795
 
7.3%
h 9473
 
5.9%
r 8647
 
5.3%
d 8166
 
5.0%
s 7950
 
4.9%
Other values (37) 41512
25.7%
Common
ValueCountFrequency (%)
18113
64.0%
, 10168
35.9%
- 19
 
0.1%
� 2
 
< 0.1%
_ 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 190122
> 99.9%
Specials 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
18113
 
9.5%
n 17601
 
9.3%
a 16702
 
8.8%
e 14520
 
7.6%
i 13615
 
7.2%
t 11839
 
6.2%
o 11795
 
6.2%
, 10168
 
5.3%
h 9473
 
5.0%
r 8647
 
4.5%
Other values (41) 57649
30.3%
Specials
ValueCountFrequency (%)
� 2
100.0%

Average Cost for two
Real number (ℝ)

SKEWED 

Distinct140
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1199.2108
Minimum0
Maximum800000
Zeros18
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size74.7 KiB
2023-08-03T11:47:52.089360image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile40
Q1250
median400
Q3700
95-th percentile1700
Maximum800000
Range800000
Interquartile range (IQR)450

Descriptive statistics

Standard deviation16121.183
Coefficient of variation (CV)13.443161
Kurtosis1495.7774
Mean1199.2108
Median Absolute Deviation (MAD)200
Skewness35.477915
Sum11453662
Variance2.5989254 × 108
MonotonicityNot monotonic
2023-08-03T11:47:52.231744image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
500 900
 
9.4%
300 897
 
9.4%
400 857
 
9.0%
200 687
 
7.2%
600 652
 
6.8%
250 461
 
4.8%
350 457
 
4.8%
700 403
 
4.2%
150 367
 
3.8%
100 353
 
3.7%
Other values (130) 3517
36.8%
ValueCountFrequency (%)
0 18
 
0.2%
7 4
 
< 0.1%
10 128
1.3%
15 4
 
< 0.1%
20 25
 
0.3%
25 174
1.8%
30 24
 
0.3%
35 17
 
0.2%
40 115
1.2%
45 12
 
0.1%
ValueCountFrequency (%)
800000 2
 
< 0.1%
500000 1
 
< 0.1%
450000 1
 
< 0.1%
350000 1
 
< 0.1%
300000 2
 
< 0.1%
250000 2
 
< 0.1%
200000 6
0.1%
165000 1
 
< 0.1%
150000 1
 
< 0.1%
120000 1
 
< 0.1%

Currency
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size74.7 KiB
Indian Rupees(Rs.)
8652 
Dollar($)
 
482
Pounds(��)
 
80
Brazilian Real(R$)
 
60
Emirati Diram(AED)
 
60
Other values (7)
 
217

Length

Max length22
Median length18
Mean length17.385823
Min length7

Characters and Unicode

Total characters166052
Distinct characters37
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBotswana Pula(P)
2nd rowBotswana Pula(P)
3rd rowBotswana Pula(P)
4th rowBotswana Pula(P)
5th rowBotswana Pula(P)

Common Values

ValueCountFrequency (%)
Indian Rupees(Rs.) 8652
90.6%
Dollar($) 482
 
5.0%
Pounds(��) 80
 
0.8%
Brazilian Real(R$) 60
 
0.6%
Emirati Diram(AED) 60
 
0.6%
Rand(R) 60
 
0.6%
NewZealand($) 40
 
0.4%
Turkish Lira(TL) 34
 
0.4%
Botswana Pula(P) 22
 
0.2%
Indonesian Rupiah(IDR) 21
 
0.2%
Other values (2) 40
 
0.4%

Length

2023-08-03T11:47:52.373505image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
indian 8652
46.9%
rupees(rs 8652
46.9%
dollar 482
 
2.6%
pounds(�� 80
 
0.4%
brazilian 60
 
0.3%
real(r 60
 
0.3%
emirati 60
 
0.3%
diram(aed 60
 
0.3%
rand(r 60
 
0.3%
newzealand 40
 
0.2%
Other values (11) 254
 
1.4%

Most occurring characters

ValueCountFrequency (%)
n 17669
10.6%
R 17666
10.6%
e 17505
10.5%
s 17461
10.5%
a 9816
 
5.9%
( 9551
 
5.8%
) 9551
 
5.8%
i 9122
 
5.5%
8909
 
5.4%
d 8853
 
5.3%
Other values (27) 39949
24.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 100942
60.8%
Uppercase Letter 27705
 
16.7%
Open Punctuation 9551
 
5.8%
Close Punctuation 9551
 
5.8%
Space Separator 8909
 
5.4%
Other Punctuation 8652
 
5.2%
Currency Symbol 582
 
0.4%
Other Symbol 160
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 17669
17.5%
e 17505
17.3%
s 17461
17.3%
a 9816
9.7%
i 9122
9.0%
d 8853
8.8%
u 8829
8.7%
p 8693
8.6%
l 1166
 
1.2%
r 770
 
0.8%
Other values (7) 1058
 
1.0%
Uppercase Letter
ValueCountFrequency (%)
R 17666
63.8%
I 8694
31.4%
D 623
 
2.2%
P 124
 
0.4%
E 120
 
0.4%
L 108
 
0.4%
B 82
 
0.3%
T 68
 
0.2%
A 60
 
0.2%
N 40
 
0.1%
Other values (4) 120
 
0.4%
Open Punctuation
ValueCountFrequency (%)
( 9551
100.0%
Close Punctuation
ValueCountFrequency (%)
) 9551
100.0%
Space Separator
ValueCountFrequency (%)
8909
100.0%
Other Punctuation
ValueCountFrequency (%)
. 8652
100.0%
Currency Symbol
ValueCountFrequency (%)
$ 582
100.0%
Other Symbol
ValueCountFrequency (%)
� 160
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 128647
77.5%
Common 37405
 
22.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 17669
13.7%
R 17666
13.7%
e 17505
13.6%
s 17461
13.6%
a 9816
7.6%
i 9122
7.1%
d 8853
6.9%
u 8829
6.9%
I 8694
6.8%
p 8693
6.8%
Other values (21) 4339
 
3.4%
Common
ValueCountFrequency (%)
( 9551
25.5%
) 9551
25.5%
8909
23.8%
. 8652
23.1%
$ 582
 
1.6%
� 160
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 165892
99.9%
Specials 160
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 17669
10.7%
R 17666
10.6%
e 17505
10.6%
s 17461
10.5%
a 9816
 
5.9%
( 9551
 
5.8%
) 9551
 
5.8%
i 9122
 
5.5%
8909
 
5.4%
d 8853
 
5.3%
Other values (26) 39789
24.0%
Specials
ValueCountFrequency (%)
� 160
100.0%

Has Table booking
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.5 KiB
False
8393 
True
1158 
ValueCountFrequency (%)
False 8393
87.9%
True 1158
 
12.1%
2023-08-03T11:47:52.543657image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Has Online delivery
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.5 KiB
False
7100 
True
2451 
ValueCountFrequency (%)
False 7100
74.3%
True 2451
 
25.7%
2023-08-03T11:47:52.667821image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Is delivering now
Boolean

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.5 KiB
False
9517 
True
 
34
ValueCountFrequency (%)
False 9517
99.6%
True 34
 
0.4%
2023-08-03T11:47:52.759084image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Switch to order menu
Boolean

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.5 KiB
False
9551 
ValueCountFrequency (%)
False 9551
100.0%
2023-08-03T11:47:52.841338image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Price range
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size74.7 KiB
1
4444 
2
3113 
3
1408 
4
586 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9551
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row4
4th row4
5th row4

Common Values

ValueCountFrequency (%)
1 4444
46.5%
2 3113
32.6%
3 1408
 
14.7%
4 586
 
6.1%

Length

2023-08-03T11:47:52.921420image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-03T11:47:53.027792image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1 4444
46.5%
2 3113
32.6%
3 1408
 
14.7%
4 586
 
6.1%

Most occurring characters

ValueCountFrequency (%)
1 4444
46.5%
2 3113
32.6%
3 1408
 
14.7%
4 586
 
6.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9551
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 4444
46.5%
2 3113
32.6%
3 1408
 
14.7%
4 586
 
6.1%

Most occurring scripts

ValueCountFrequency (%)
Common 9551
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 4444
46.5%
2 3113
32.6%
3 1408
 
14.7%
4 586
 
6.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9551
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 4444
46.5%
2 3113
32.6%
3 1408
 
14.7%
4 586
 
6.1%

Aggregate rating
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct33
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.66637
Minimum0
Maximum4.9
Zeros2148
Zeros (%)22.5%
Negative0
Negative (%)0.0%
Memory size74.7 KiB
2023-08-03T11:47:53.131665image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12.5
median3.2
Q33.7
95-th percentile4.3
Maximum4.9
Range4.9
Interquartile range (IQR)1.2

Descriptive statistics

Standard deviation1.5163775
Coefficient of variation (CV)0.56870484
Kurtosis-0.58221714
Mean2.66637
Median Absolute Deviation (MAD)0.5
Skewness-0.95413047
Sum25466.5
Variance2.2994008
MonotonicityNot monotonic
2023-08-03T11:47:53.252636image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
0 2148
22.5%
3.2 522
 
5.5%
3.1 519
 
5.4%
3.4 498
 
5.2%
3.3 483
 
5.1%
3.5 480
 
5.0%
3 468
 
4.9%
3.6 458
 
4.8%
3.7 427
 
4.5%
3.8 400
 
4.2%
Other values (23) 3148
33.0%
ValueCountFrequency (%)
0 2148
22.5%
1.8 1
 
< 0.1%
1.9 2
 
< 0.1%
2 7
 
0.1%
2.1 15
 
0.2%
2.2 27
 
0.3%
2.3 47
 
0.5%
2.4 87
 
0.9%
2.5 110
 
1.2%
2.6 191
 
2.0%
ValueCountFrequency (%)
4.9 61
 
0.6%
4.8 25
 
0.3%
4.7 42
 
0.4%
4.6 78
 
0.8%
4.5 95
 
1.0%
4.4 144
1.5%
4.3 174
1.8%
4.2 221
2.3%
4.1 274
2.9%
4 266
2.8%

Rating color
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size74.7 KiB
Orange
3737 
White
2148 
Yellow
2100 
Green
1079 
Dark Green
 
301

Length

Max length10
Median length6
Mean length5.7297665
Min length3

Characters and Unicode

Total characters54725
Distinct characters20
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDark Green
2nd rowDark Green
3rd rowGreen
4th rowDark Green
5th rowDark Green

Common Values

ValueCountFrequency (%)
Orange 3737
39.1%
White 2148
22.5%
Yellow 2100
22.0%
Green 1079
 
11.3%
Dark Green 301
 
3.2%
Red 186
 
1.9%

Length

2023-08-03T11:47:53.372159image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-03T11:47:53.493568image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
orange 3737
37.9%
white 2148
21.8%
yellow 2100
21.3%
green 1380
 
14.0%
dark 301
 
3.1%
red 186
 
1.9%

Most occurring characters

ValueCountFrequency (%)
e 10931
20.0%
r 5418
9.9%
n 5117
9.4%
l 4200
 
7.7%
a 4038
 
7.4%
O 3737
 
6.8%
g 3737
 
6.8%
W 2148
 
3.9%
h 2148
 
3.9%
i 2148
 
3.9%
Other values (10) 11103
20.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 44572
81.4%
Uppercase Letter 9852
 
18.0%
Space Separator 301
 
0.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 10931
24.5%
r 5418
12.2%
n 5117
11.5%
l 4200
 
9.4%
a 4038
 
9.1%
g 3737
 
8.4%
h 2148
 
4.8%
i 2148
 
4.8%
t 2148
 
4.8%
w 2100
 
4.7%
Other values (3) 2587
 
5.8%
Uppercase Letter
ValueCountFrequency (%)
O 3737
37.9%
W 2148
21.8%
Y 2100
21.3%
G 1380
 
14.0%
D 301
 
3.1%
R 186
 
1.9%
Space Separator
ValueCountFrequency (%)
301
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 54424
99.4%
Common 301
 
0.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 10931
20.1%
r 5418
10.0%
n 5117
9.4%
l 4200
 
7.7%
a 4038
 
7.4%
O 3737
 
6.9%
g 3737
 
6.9%
W 2148
 
3.9%
h 2148
 
3.9%
i 2148
 
3.9%
Other values (9) 10802
19.8%
Common
ValueCountFrequency (%)
301
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 54725
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 10931
20.0%
r 5418
9.9%
n 5117
9.4%
l 4200
 
7.7%
a 4038
 
7.4%
O 3737
 
6.8%
g 3737
 
6.8%
W 2148
 
3.9%
h 2148
 
3.9%
i 2148
 
3.9%
Other values (10) 11103
20.3%

Rating text
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size74.7 KiB
Average
3737 
Not rated
2148 
Good
2100 
Very Good
1079 
Excellent
 
301

Length

Max length9
Median length7
Mean length7.0207308
Min length4

Characters and Unicode

Total characters67055
Distinct characters20
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowExcellent
2nd rowExcellent
3rd rowVery Good
4th rowExcellent
5th rowExcellent

Common Values

ValueCountFrequency (%)
Average 3737
39.1%
Not rated 2148
22.5%
Good 2100
22.0%
Very Good 1079
 
11.3%
Excellent 301
 
3.2%
Poor 186
 
1.9%

Length

2023-08-03T11:47:53.609495image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-03T11:47:53.730265image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
average 3737
29.2%
good 3179
24.9%
not 2148
16.8%
rated 2148
16.8%
very 1079
 
8.4%
excellent 301
 
2.4%
poor 186
 
1.5%

Most occurring characters

ValueCountFrequency (%)
e 11303
16.9%
o 8878
13.2%
r 7150
10.7%
a 5885
8.8%
d 5327
7.9%
t 4597
6.9%
v 3737
 
5.6%
A 3737
 
5.6%
g 3737
 
5.6%
3227
 
4.8%
Other values (10) 9477
14.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 53198
79.3%
Uppercase Letter 10630
 
15.9%
Space Separator 3227
 
4.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 11303
21.2%
o 8878
16.7%
r 7150
13.4%
a 5885
11.1%
d 5327
10.0%
t 4597
8.6%
v 3737
 
7.0%
g 3737
 
7.0%
y 1079
 
2.0%
l 602
 
1.1%
Other values (3) 903
 
1.7%
Uppercase Letter
ValueCountFrequency (%)
A 3737
35.2%
G 3179
29.9%
N 2148
20.2%
V 1079
 
10.2%
E 301
 
2.8%
P 186
 
1.7%
Space Separator
ValueCountFrequency (%)
3227
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 63828
95.2%
Common 3227
 
4.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 11303
17.7%
o 8878
13.9%
r 7150
11.2%
a 5885
9.2%
d 5327
8.3%
t 4597
7.2%
v 3737
 
5.9%
A 3737
 
5.9%
g 3737
 
5.9%
G 3179
 
5.0%
Other values (9) 6298
9.9%
Common
ValueCountFrequency (%)
3227
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 67055
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 11303
16.9%
o 8878
13.2%
r 7150
10.7%
a 5885
8.8%
d 5327
7.9%
t 4597
6.9%
v 3737
 
5.6%
A 3737
 
5.6%
g 3737
 
5.6%
3227
 
4.8%
Other values (10) 9477
14.1%

Votes
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1012
Distinct (%)10.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean156.90975
Minimum0
Maximum10934
Zeros1094
Zeros (%)11.5%
Negative0
Negative (%)0.0%
Memory size74.7 KiB
2023-08-03T11:47:53.864065image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15
median31
Q3131
95-th percentile699
Maximum10934
Range10934
Interquartile range (IQR)126

Descriptive statistics

Standard deviation430.16915
Coefficient of variation (CV)2.7415068
Kurtosis128.22597
Mean156.90975
Median Absolute Deviation (MAD)30
Skewness8.8076367
Sum1498645
Variance185045.49
MonotonicityNot monotonic
2023-08-03T11:47:54.272246image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1094
 
11.5%
1 483
 
5.1%
2 327
 
3.4%
3 244
 
2.6%
4 207
 
2.2%
7 168
 
1.8%
5 164
 
1.7%
6 154
 
1.6%
10 135
 
1.4%
8 134
 
1.4%
Other values (1002) 6441
67.4%
ValueCountFrequency (%)
0 1094
11.5%
1 483
5.1%
2 327
 
3.4%
3 244
 
2.6%
4 207
 
2.2%
5 164
 
1.7%
6 154
 
1.6%
7 168
 
1.8%
8 134
 
1.4%
9 113
 
1.2%
ValueCountFrequency (%)
10934 1
< 0.1%
9667 1
< 0.1%
7931 1
< 0.1%
7574 1
< 0.1%
6907 1
< 0.1%
5966 1
< 0.1%
5705 1
< 0.1%
5434 1
< 0.1%
5385 1
< 0.1%
5288 1
< 0.1%

Interactions

2023-08-03T11:47:40.532283image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-03T11:47:33.535830image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-03T11:47:34.597596image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-03T11:47:35.569930image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-03T11:47:36.588035image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-03T11:47:38.394053image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-03T11:47:39.535436image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-03T11:47:40.690171image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-03T11:47:33.741112image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-03T11:47:34.746465image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-03T11:47:35.712422image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-03T11:47:36.739621image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-03T11:47:38.548704image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-03T11:47:39.696093image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-03T11:47:40.819517image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-03T11:47:33.873631image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-03T11:47:34.871969image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-03T11:47:35.848414image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-03T11:47:36.877835image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-03T11:47:38.691418image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-03T11:47:39.828345image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-03T11:47:40.959737image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-03T11:47:34.023264image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-03T11:47:35.013093image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-03T11:47:35.991843image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-03T11:47:37.015376image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-03T11:47:38.842409image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-03T11:47:39.969809image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-03T11:47:41.102522image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-03T11:47:34.153887image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-03T11:47:35.143525image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-03T11:47:36.144624image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-03T11:47:37.153978image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-03T11:47:38.983998image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-03T11:47:40.105657image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-03T11:47:41.258368image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-03T11:47:34.313087image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-03T11:47:35.304308image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-03T11:47:36.295723image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-03T11:47:37.302833image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-03T11:47:39.141591image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-03T11:47:40.252903image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-03T11:47:41.397813image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-03T11:47:34.453419image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-03T11:47:35.435619image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-03T11:47:36.441273image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-03T11:47:37.443250image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-03T11:47:39.338801image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-08-03T11:47:40.390155image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2023-08-03T11:47:54.404658image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Restaurant IDCountry CodeLongitudeLatitudeAverage Cost for twoAggregate ratingVotesCurrencyHas Table bookingHas Online deliveryIs delivering nowPrice rangeRating colorRating text
Restaurant ID1.0000.052-0.055-0.115-0.182-0.244-0.4760.4580.1410.1690.0000.2130.2670.267
Country Code0.0521.000-0.3440.094-0.4010.3930.3080.9720.0890.1620.0000.2280.2270.227
Longitude-0.055-0.3441.0000.0290.170-0.139-0.0790.7360.1290.2230.0000.2140.2380.238
Latitude-0.1150.0940.0291.000-0.219-0.056-0.0180.6850.1030.2070.0000.2460.2460.246
Average Cost for two-0.182-0.4010.170-0.2191.0000.2200.3460.3670.0000.0000.0000.0580.0600.060
Aggregate rating-0.2440.393-0.139-0.0560.2201.0000.8460.1970.2090.3050.0300.3171.0001.000
Votes-0.4760.308-0.079-0.0180.3460.8461.0000.0230.1340.0300.0000.1330.1500.150
Currency0.4580.9720.7360.6850.3670.1970.0231.0000.1300.1820.0000.2370.2340.234
Has Table booking0.1410.0890.1290.1030.0000.2090.1340.1301.0000.1000.0100.5430.2080.208
Has Online delivery0.1690.1620.2230.2070.0000.3050.0300.1820.1001.0000.0990.2740.3040.304
Is delivering now0.0000.0000.0000.0000.0000.0300.0000.0000.0100.0991.0000.0370.0320.032
Price range0.2130.2280.2140.2460.0580.3170.1330.2370.5430.2740.0371.0000.3160.316
Rating color0.2670.2270.2380.2460.0601.0000.1500.2340.2080.3040.0320.3161.0001.000
Rating text0.2670.2270.2380.2460.0601.0000.1500.2340.2080.3040.0320.3161.0001.000

Missing values

2023-08-03T11:47:41.637894image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-08-03T11:47:41.977036image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Restaurant IDRestaurant NameCountry CodeCityAddressLocalityLocality VerboseLongitudeLatitudeCuisinesAverage Cost for twoCurrencyHas Table bookingHas Online deliveryIs delivering nowSwitch to order menuPrice rangeAggregate ratingRating colorRating textVotes
06317637Le Petit Souffle162Makati CityThird Floor, Century City Mall, Kalayaan Avenue, Poblacion, Makati CityCentury City Mall, Poblacion, Makati CityCentury City Mall, Poblacion, Makati City, Makati City121.02753514.565443French, Japanese, Desserts1100Botswana Pula(P)YesNoNoNo34.8Dark GreenExcellent314
16304287Izakaya Kikufuji162Makati CityLittle Tokyo, 2277 Chino Roces Avenue, Legaspi Village, Makati CityLittle Tokyo, Legaspi Village, Makati CityLittle Tokyo, Legaspi Village, Makati City, Makati City121.01410114.553708Japanese1200Botswana Pula(P)YesNoNoNo34.5Dark GreenExcellent591
26300002Heat - Edsa Shangri-La162Mandaluyong CityEdsa Shangri-La, 1 Garden Way, Ortigas, Mandaluyong CityEdsa Shangri-La, Ortigas, Mandaluyong CityEdsa Shangri-La, Ortigas, Mandaluyong City, Mandaluyong City121.05683114.581404Seafood, Asian, Filipino, Indian4000Botswana Pula(P)YesNoNoNo44.4GreenVery Good270
36318506Ooma162Mandaluyong CityThird Floor, Mega Fashion Hall, SM Megamall, Ortigas, Mandaluyong CitySM Megamall, Ortigas, Mandaluyong CitySM Megamall, Ortigas, Mandaluyong City, Mandaluyong City121.05647514.585318Japanese, Sushi1500Botswana Pula(P)NoNoNoNo44.9Dark GreenExcellent365
46314302Sambo Kojin162Mandaluyong CityThird Floor, Mega Atrium, SM Megamall, Ortigas, Mandaluyong CitySM Megamall, Ortigas, Mandaluyong CitySM Megamall, Ortigas, Mandaluyong City, Mandaluyong City121.05750814.584450Japanese, Korean1500Botswana Pula(P)YesNoNoNo44.8Dark GreenExcellent229
518189371Din Tai Fung162Mandaluyong CityGround Floor, Mega Fashion Hall, SM Megamall, Ortigas, Mandaluyong CitySM Megamall, Ortigas, Mandaluyong CitySM Megamall, Ortigas, Mandaluyong City, Mandaluyong City121.05631414.583764Chinese1000Botswana Pula(P)NoNoNoNo34.4GreenVery Good336
66300781Buffet 101162Pasay CityBuilding K, SM By The Bay, Sunset Boulevard, Mall of Asia Complex (MOA), Pasay CitySM by the Bay, Mall of Asia Complex, Pasay CitySM by the Bay, Mall of Asia Complex, Pasay City, Pasay City120.97966714.531333Asian, European2000Botswana Pula(P)YesNoNoNo44.0GreenVery Good520
76301290Vikings162Pasay CityBuilding B, By The Bay, Seaside Boulevard, Mall of Asia Complex (MOA), Pasay CitySM by the Bay, Mall of Asia Complex, Pasay CitySM by the Bay, Mall of Asia Complex, Pasay City, Pasay City120.97933314.540000Seafood, Filipino, Asian, European2000Botswana Pula(P)YesNoNoNo44.2GreenVery Good677
86300010Spiral - Sofitel Philippine Plaza Manila162Pasay CityPlaza Level, Sofitel Philippine Plaza Manila, CCP Complex, Pasay CitySofitel Philippine Plaza Manila, Pasay CitySofitel Philippine Plaza Manila, Pasay City, Pasay City120.98009014.552990European, Asian, Indian6000Botswana Pula(P)YesNoNoNo44.9Dark GreenExcellent621
96314987Locavore162Pasig CityBrixton Technology Center, 10 Brixton Street, Kapitolyo, Pasig CityKapitolyoKapitolyo, Pasig City121.05653214.572041Filipino1100Botswana Pula(P)YesNoNoNo34.8Dark GreenExcellent532
Restaurant IDRestaurant NameCountry CodeCityAddressLocalityLocality VerboseLongitudeLatitudeCuisinesAverage Cost for twoCurrencyHas Table bookingHas Online deliveryIs delivering nowSwitch to order menuPrice rangeAggregate ratingRating colorRating textVotes
95415905215Emirgan S�_ti��208��stanbulEmirgan Mahallesi, Sak۱p Sabanc۱ Caddesi, No 46, Sar۱yer, ��stanbulEmirg��nEmirg��n, ��stanbul29.05662041.104969Restaurant Cafe, Turkish, Desserts75Turkish Lira(TL)NoNoNoNo34.2GreenVery Good877
95425926979Leman K�_lt�_r208��stanbulCafera��a Mahallesi, Ne��et �_mer Sokak, No 9/A, Kad۱k�_y, ��stanbulKad۱k�_y MerkezKad۱k�_y Merkez, ��stanbul29.02280540.989705Restaurant Cafe80Turkish Lira(TL)NoNoNoNo33.7YellowGood506
95435916085Dem Karak�_y208��stanbulKemanke�� Karamustafa Pa��a Mahallesi, Hoca Tahsin Sokak, No 17, Beyo��lu, ��stanbulKarak�_yKarak�_y, ��stanbul28.97823741.024633Cafe35Turkish Lira(TL)NoNoNoNo24.5Dark GreenExcellent761
95445915547Karak�_y G�_ll�_o��lu208��stanbulKemanke�� Karamustafa Pa��a Mahallesi, R۱ht۱m Caddesi, Katl۱ Otopark Alt۱, No 4, Beyo��lu, ��stanbulKarak�_yKarak�_y, ��stanbul28.97763641.022904Desserts, B�_rek40Turkish Lira(TL)NoNoNoNo24.7Dark GreenExcellent1305
95455915054Baltazar208��stanbulKemanke�� Karamustafa Pa��a Mahallesi, K۱l۱�_ Ali Pa��a Mescidi Sokak, No 12/A, Beyo��lu, ��stanbulKarak�_yKarak�_y, ��stanbul28.98110341.025785Burger, Izgara90Turkish Lira(TL)NoNoNoNo34.3GreenVery Good870
95465915730Naml۱ Gurme208��stanbulKemanke�� Karamustafa Pa��a Mahallesi, R۱ht۱m Caddesi, No 1/1, Katl۱ Otopark Alt۱, Beyo��lu, ��stanbulKarak�_yKarak�_y, ��stanbul28.97739241.022793Turkish80Turkish Lira(TL)NoNoNoNo34.1GreenVery Good788
95475908749Ceviz A��ac۱208��stanbulKo��uyolu Mahallesi, Muhittin ��st�_nda�� Caddesi, No 85, Kad۱k�_y, ��stanbulKo��uyoluKo��uyolu, ��stanbul29.04129741.009847World Cuisine, Patisserie, Cafe105Turkish Lira(TL)NoNoNoNo34.2GreenVery Good1034
95485915807Huqqa208��stanbulKuru�_e��me Mahallesi, Muallim Naci Caddesi, No 56, Be��ikta��, ��stanbulKuru�_e��meKuru�_e��me, ��stanbul29.03464041.055817Italian, World Cuisine170Turkish Lira(TL)NoNoNoNo43.7YellowGood661
95495916112A���k Kahve208��stanbulKuru�_e��me Mahallesi, Muallim Naci Caddesi, No 64/B, Be��ikta��, ��stanbulKuru�_e��meKuru�_e��me, ��stanbul29.03601941.057979Restaurant Cafe120Turkish Lira(TL)NoNoNoNo44.0GreenVery Good901
95505927402Walter's Coffee Roastery208��stanbulCafea��a Mahallesi, Bademalt۱ Sokak, No 21/B, Kad۱k�_y, ��stanbulModaModa, ��stanbul29.02601640.984776Cafe55Turkish Lira(TL)NoNoNoNo24.0GreenVery Good591